This disclosure relates generally to the field of image processing and, more particularly, to techniques for generating global tone-mapping operators (aka tone mapping curves).
High Dynamic Range (HDR) images are formed by blending together multiple exposures of a common scene. Use of HDR techniques permit a large range of intensities in the original scene to be recorded (such is not the case for typical camera images where highlights and shadows are often clipped). Many display devices such as monitors and printers however, cannot accommodate the large dynamic range present in a HDR image. To visualize HDR images on devices such as these, dynamic range compression is effected by one or more Tone-Mapping Operators (TMOs). In general, there are two types of TMOs: global (spatially-uniform) and local (spatially-varying).
Global TMOs (G-TMOs) are non-linear subjective functions that map an input HDR image to an output Low Dynamic Range (LDR) image. G-TMO functions are typically parameterized by image statistics drawn from the input image. Once a G-TMO function is defined, every pixel in an input image is mapped globally (independent from surrounding pixels in the image). By their very nature, G-TMOs compress or expand the dynamic range of the input signal (i.e., image). By way of example, if the slope of a G-TMO function is less than 1 the image's detail is compressed in the output image. Such compression often occurs in highlight areas of an image and, when this happens, the output image appears flat; G-TMOs often produce images lacking in contrast.
Spatially-varying TMOs (SV-TMOs) on the other hand, take into account the spatial context within an image when mapping input pixel values to output pixel values. Parameters of a nonlinear SV-TMO function can change at each pixel according to the local features extracted from neighboring pixels. This often leads to improved local contrast. It is known, however, that strong SV-TMOs can generate halo artifacts in output images (e.g., intensity inversions near high contrast edges). Weaker SV-TMOs, while avoiding such halo artifacts, typically mute image detail (compared to the original, or input, image). As used herein, a “strong” SV-TMO is one in which local processing is significant compared to a “weak” SV-TMO (which, in the limit, tends toward output similar to that of a G-TMO). Still, it is generally recognized that people feel images mapped using SV-TMOs are more appealing than the same images mapped using G-TMOs. On the downside, SV-TMOs are generally far more complicated to implement than G-TMOs. Thus, there is a need for a fast executing global tone-mapping operator that is able to produce appealing output images (comparable to those produced by spatially variable tone-mapping operators).